Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming

This paper proposes an approximate dynamic programming (ADP)-based approach for the economic dispatch (ED) of microgrid with distributed generations. The time-variant renewable generation, electricity price, and the power demand are considered as stochastic variables in this paper. An ADP based ED (...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 10; no. 3; pp. 2440 - 2452
Main Authors: Shuai, Hang, Fang, Jiakun, Ai, Xiaomeng, Tang, Yufei, Wen, Jinyu, He, Haibo
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
approximate dynamic programming (ADP)
Batteries
Computer simulation
Distributed generation
Dynamic programming
economic dispatch (ED)
Economics
Electricity consumption
Electricity pricing
Energy management
Error reduction
Generators
Linear functions
Microgrid
Microgrids
Monte Carlo simulation
Optimization
Power dispatch
Prediction algorithms
stochastic optimization
Uncertainty
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:This paper proposes an approximate dynamic programming (ADP)-based approach for the economic dispatch (ED) of microgrid with distributed generations. The time-variant renewable generation, electricity price, and the power demand are considered as stochastic variables in this paper. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2018.2798039